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The random Fourier Features method has been found very effective in approximating the kernel functions. Our former studies show that through a mixing mechanism of the feature space formed by random Fourier features and certain linear algorithms, the fuzzy clustering results in the approximated feature space are comparable to or even exceed the classical kernel-based algorithms. To increase the robustness...
Data explosion drives data analysis tools to update faster and faster, while clustering plays an indispensable role in knowledge discovery. Whereas, most of the clustering algorithms only effect on those linear separable data. Kernel-based clustering methods perform well on data sets with non-linear inner structure, but at the same time, the requirement of large memory and running time induce poor...
Although fuzzy c-means algorithm has shown great capability to spherical clusters, it can not perform very well on non-spherical data sets yet. To deal with this problem, kernel-based fuzzy clustering has been presented by mapping data points into a high-dimensional Hilbert space with kernel functions. However, the computational complexity of kernel matrix is always quadratic, usually makes kernel...
This manuscript introduces a new clustering based image segmentation method. By implanting the concept of shadowed set in the estimation procedure for cluster centers, one new algorithm named shadowed modified C-mean algorithm (SMFCM) is proposed. The results on noise image segmentation demonstrate the shadowed modified fuzzy C-mean is better than some traditional approaches when the noise rate is...
In order to apply successfully the fuzzy clustering algorithms like shadowed C-means (SCM) to image segmentation problems, the spatial information related with each pixel in the image should be carefully calculated and appended to the clustering algorithms. In this paper, the non-local spatial information calculation is introduced to SCM. Because the data in the kernel space demonstrate more linearly-separable...
A new shadowed c-means clustering based image segmentation method is proposed in this paper. By including the local spatial information in shadowed c-means algorithm and mapping the original data into a high dimensional space via kernel method, we propose the Kernel Spatial Shadowed C-Means (KSSCM) clustering algorithm for image segmentation problems. The KSSCM based approach shows better performance...
This paper introduces some new image segmentation methods in the framework of shadowed c-means clustering. By implanting the local and non-local spatial information in the membership value estimation procedure, we propose the Local Spatial Shadowed C-Means (LSSCM) algorithm, Non-local Spatial Shadowed C-Means (NLSSCM) algorithm and their combination - L+NLSSCM. Compared to traditional fuzzy c-means...
Proximity-based fuzzy c-means algorithm (P-FCM), a classical semi-supervised clustering algorithm, concerns with the number of proximity “hints” or constraints that specify an extent to which some pairs of instances are considered similar or. By replacing the fuzzy c-means in P-FCM with a kernel fuzzy c-means, this paper proposes a new semi-supervised clustering algorithm named proximity-based kernel...
Linguistic interface is a group of linguistic terms or fuzzy descriptions that describe variables in a system utilizing corresponding membership functions. Its transparency completely or partly decides the interpretability of fuzzy models. This paper proposes a GRadiEnt-descEnt-based Transparent lInguistic iNterface Generation (GREETING) approach to overcome the disadvantage of traditional linguistic...
Fuzzy neural network, which is based on fuzzy theory and BP neural network, plays an important role in practical. But the difficulty is how to construct its structure model. In this paper, according to the hypostasis of hidden layer, a fuzzy neural network model based on fuzzy clustering is brought forward, in which features of the samples are extracted and output information is synthetically considered...
In this paper, a new approach to generate decision tree from those examples with interval-valued attributes is presented and then rule matching is made. Considering that the interval values of the same attribute of all examples probably fall into certain distributing rule so as to form some center points, we cluster the interval-valued attributes of all examples by using the algorithm of FCCID (fuzzy...
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